Abstract: Uncovering the community structure exhibited by real networks is a crucialstep towards an understanding of complex systems that goes beyond the localorganization of their constituents. Many algorithms have been proposed so far,but none of them has been subjected to strict tests to evaluate theirperformance. Most of the sporadic tests performed so far involved smallnetworks with known community structure and-or artificial graphs with asimplified structure, which is very uncommon in real systems. Here we testseveral methods against a recently introduced class of benchmark graphs, withheterogeneous distributions of degree and community size. The methods are alsotested against the benchmark by Girvan and Newman and on random graphs. As aresult of our analysis, three recent algorithms introduced by Rosvall andBergstrom, Blondel et al. and Ronhovde and Nussinov, respectively, have anexcellent performance, with the additional advantage of low computationalcomplexity, which enables one to analyze large systems.